Computational processing and error reduction strategies for standardized quantitative data in biological networks

Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

BlotIt—Optimal alignment of Western blot and qPCR experiments

Biological systems are frequently analyzed by means of mechanistic mathematical models. In order to infer model parameters and provide a useful model that can be employed for systems understanding and hypothesis testing, the model is often calibrated on quantitative, time-resolved data. To do so, it is typically important to compare experimental measurements over broad time ranges and various experimental conditions, e.g. perturbations of the biological system. However, most of the established experimental techniques such as Western blot, or quantitative real-time polymerase chain reaction only provide measurements on a relative scale, since different sample volumes, experimental adjustments or varying development times of a gel lead to systematic shifts in the data. In turn, the number of measurements corresponding to the same scale enabling comparability is limited. Here, we present a new flexible method to align measurement data that obeys different scaling factors and compare it to existing normalization approaches. We propose an alignment model to estimate these scaling factors and provide the possibility to adapt this model depending on the measurement technique of interest. In addition, an error model can be specified to adequately weight the different data points and obtain scaling-model based confidence intervals of the finally scaled data points. Our approach is applicable to all sorts of relative measurements and does not need a particular experimental condition that has been measured over all available scales. An implementation of the method is provided with the R package blotIt including refined ways of visualization.

Identification of Interleukin1β as an Amplifier of Interferon alpha-induced Antiviral Responses

The induction of an interferon-mediated response is the first line of defense against pathogens such as viruses. Yet, the dynamics and extent of interferon alpha (IFNα)-induced antiviral genes vary remarkably and comprise three expression clusters: early, intermediate and late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability as well as a negative regulatory loop as key mechanisms endogenously controlling the expression dynamics of IFNα-induced antiviral genes in hepatocytes. Guided by the mathematical model, we uncovered that this regulatory loop is mediated by the transcription factor IRF2 and showed that knock-down of IRF2 results in enhanced expression of early, intermediate and late IFNα-induced antiviral genes. Co-stimulation experiments with different pro-inflammatory cytokines revealed that this amplified expression dynamics of the early, intermediate and late IFNα-induced antiviral genes can also be achieved by co-application of IFNα and interleukin1 beta (IL1β). Consistently, we found that IL1β enhances IFNα-mediated repression of viral replication. Conversely, we observed that in IL1β receptor knock-out mice replication of viruses sensitive to IFNα is increased. Thus, IL1β is capable to potentiate IFNα-induced antiviral responses and could be exploited to improve antiviral therapies.

Innate immune responses contribute to the control of viral infections and the induction of interferon alpha (IFNα)-mediated antiviral responses is an important component. However, IFNα induces a multitude of antiviral response genes and the expression dynamics of these genes can be classified as early, intermediate and late. Here we show, based on a mathematical modeling approach, that mRNA stability as well as the negative regulator IRF2 control the expression dynamics of IFNα-induced antiviral genes. Knock-down of IRF2 resulted in the amplified IFNα-mediated induction of the antiviral genes and this amplified expression of antiviral genes could be functionally mimicked by co-stimulation with IFNα and IL1β. We observed that co-stimulation with IFNα and IL1β enhanced the repression of virus replication and that knock-out of the IL1 receptor in mice resulted in increased replication of a virus sensitive to IFNα. In sum, our studies identified IL1β as an important amplifier of IFNα-induced antiviral responses.

Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.

Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction

Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.

Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases

Extracellular growth factors signal to transcription factors via a limited number of cytoplasmic kinase cascades. It remains unclear how such cascades encode ligand identities and concentrations. In this paper, we use live-cell imaging and statistical modeling to study FOXO3, a transcription factor regulating diverse aspects of cellular physiology that is under combinatorial control. We show that FOXO3 nuclear-to-cytosolic translocation has two temporally distinct phases varying in magnitude with growth factor identity and cell type. These phases comprise synchronous translocation soon after ligand addition followed by an extended back-and-forth shuttling; this shuttling is pulsatile and does not have a characteristic frequency, unlike a simple oscillator. Early and late dynamics are differentially regulated by Akt and ERK and have low mutual information, potentially allowing the two phases to encode different information. In cancer cells in which ERK and Akt are dysregulated by oncogenic mutation, the diversity of states is lower.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.

IL-1β-induced and p38MAPK-dependent activation of the mitogen-activated protein kinase-activated protein kinase 2 (MK2) in hepatocytes: Signal transduction with robust and concentration-independent signal amplification

The IL-1β induced activation of the p38^MAPK/MAPK-activated protein kinase 2 (MK2) pathway in hepatocytes is important for control of the acute phase response and regulation of liver regeneration. Many aspects of the regulatory relevance of this pathway have been investigated in immune cells in the context of inflammation. However, very little is known about concentration-dependent activation kinetics and signal propagation in hepatocytes and the role of MK2. We established a mathematical model for IL-1β-induced activation of the p38^MAPK/MK2 pathway in hepatocytes that was calibrated to quantitative data on time- and IL-1β concentration-dependent phosphorylation of p38^MAPK and MK2 in primary mouse hepatocytes. This analysis showed that, in hepatocytes, signal transduction from IL-1β via p38^MAPK to MK2 is characterized by strong signal amplification. Quantification of p38^MAPK and MK2 revealed that, in hepatocytes, at maximum, 11.3% of p38^MAPK molecules and 36.5% of MK2 molecules are activated in response to IL-1β. The mathematical model was experimentally validated by employing phosphatase inhibitors and the p38^MAPK inhibitor SB203580. Model simulations predicted an IC₅₀ of 1-1.2 μm for SB203580 in hepatocytes. In silico analyses and experimental validation demonstrated that the kinase activity of p38^MAPK determines signal amplitude, whereas phosphatase activity affects both signal amplitude and duration. p38^MAPK and MK2 concentrations and responsiveness toward IL-1β were quantitatively compared between hepatocytes and macrophages. In macrophages, the absolute p38^MAPK and MK2 concentration was significantly higher. Finally, in line with experimental observations, the mathematical model predicted a significantly higher half-maximal effective concentration for IL-1β-induced pathway activation in macrophages compared with hepatocytes, underscoring the importance of cell type-specific differences in pathway regulation.

Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L₁ regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types.

A major challenge in the development of therapeutic interventions is the selective inhibition of a signal transduction pathway in one cell type such as a cancer cell leaving the other cell type such as a healthy cell as unaffected as possible. Here, we propose a new approach that combines mathematical modeling based on quantitative experimental data with statistical methods. We demonstrate based on simulated data that our approach can determine which parameters are the same and which parameters differ in two exemplary cell types. We compare a lung cancer cell line to the precursor cells of red blood cells. We show that the same signal transduction network induced by erythropoietin (EPO), a hormone that is frequently employed to treat anemia in cancer patients, regulates survival of both cell types. Based on our experimental data in combination with our computational approach, we identify seven cell type-specific differences in this signaling pathway. Our strategy allows predicting therapeutic targets that could be inhibited to interfere with survival of lung cancer cells while leaving production of red blood cells unaffected.

T160‐phosphorylated CDK2 defines threshold for HGF dependent proliferation in primary hepatocytes

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

Context-specific flow through the MEK/ERK module produces cell- and ligand-specific patterns of ERK single and double phosphorylation

The same pathway, such as the mitogen-activated protein kinase (MAPK) pathway, can produce different cellular responses, depending on stimulus or cell type. We examined the phosphorylation dynamics of the MAPK kinase MEK and its targets extracellular signal-regulated kinase 1 and 2 (ERK1/2) in primary hepatocytes and the transformed keratinocyte cell line HaCaT A5 exposed to either hepatocyte growth factor or interleukin-6. By combining quantitative mass spectrometry with dynamic modeling, we elucidated network structures for the reversible threonine and tyrosine phosphorylation of ERK in both cell types. In addition to differences in the phosphorylation and dephosphorylation reactions, the HaCaT network model required two feedback mechanisms, which, as the experimental data suggested, involved the induction of the dual-specificity phosphatase DUSP6 and the scaffold paxillin. We assayed and modeled the accumulation of the double-phosphorylated and active form of ERK1/2, as well as the dynamics of the changes in the monophosphorylated forms of ERK1/2. Modeling the differences in the dynamics of the changes in the distributions of the phosphorylated forms of ERK1/2 suggested that different amounts of MEK activity triggered context-specific responses, with primary hepatocytes favoring the formation of double-phosphorylated ERK1/2 and HaCaT A5 cells that produce both the threonine-phosphorylated and the double-phosphorylated form. These differences in phosphorylation distributions explained the threshold, sensitivity, and saturation of the ERK response. We extended the findings of differential ERK phosphorylation profiles to five additional cultured cell systems and matched liver tumor and normal tissue, which revealed context-specific patterns of the various forms of phosphorylated ERK.

Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling

Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.

Methodological issues in the quantification of subgingival microorganisms using the checkerboard technique

The reproducibility and reliability of quantitative microbiological assessments using the DNA-DNA hybridization "checkerboard method" (CKB) were assessed. The data originated from 180 chronic periodontitis patients, who were enrolled in a clinical trial and sampled at baseline, and 3 and 12m post-therapy. The samples were divided into two portions allowing evaluation of reproducibility. In total, 531 samples were analyzed in a first run, using standard bacterial preparations of cells and 513 samples were accessible for analysis in the second, using standards based on purified DNA from the species. The microbial probe panel consisted of periodontitis marker bacteria as well as non-oral microorganisms. Three different ways of quantifying and presenting data; the visual scoring method, VSM, the standard curve method, SCM, and the percent method, PM, were compared. The second set of analyses based on the use of standard preparations of pure DNA was shown to be more consistent than the first set using standards based on cells, while the effect of storage time per se up to 2.5y seemed to be marginal. The best reproducibility was found for Tannerella forsythia, irrespective of quantification technique (Spearman's rho=0.587, Pearson's r≥0.540). The percent method (PM) based on percent of High Standard (10(6) cells) was more reliable than SCM based on a linear calibration of the High Standard and a Low Standard (10(5) cells). It was concluded that the reproducibility of the CBK method varied between different bacteria. High quality and pure specific DNA whole genomic probes and standards may have a stronger impact on the precision of the data than storage time and conditions.

Multistrip Western blotting: a tool for comparative quantitative analysis of multiple proteins

The qualitative and quantitative measurements of protein abundance and modification states are essential in understanding their functions in diverse cellular processes. Typical Western blotting, though sensitive, is prone to produce substantial errors and is not readily adapted to high-throughput technologies. Multistrip Western blotting is a modified immunoblotting procedure based on simultaneous electrophoretic transfer of proteins from multiple strips of polyacrylamide gels to a single membrane sheet. In comparison with the conventional technique, Multistrip Western blotting increases data output per single blotting cycle up to tenfold; allows concurrent measurement of up to nine different total and/or posttranslationally modified protein expression obtained from the same loading of the sample; and substantially improves the data accuracy by reducing immunoblotting-derived signal errors. This approach enables statistically reliable comparison of different or repeated sets of data and therefore is advantageous to apply in biomedical diagnostics, systems biology, and cell signaling research.

A rapidly reversible chemical dimerizer system to study lipid signaling in living cells

Chemical dimerizers are powerful tools for non-invasive manipulation of enzyme activities in intact cells. Here we introduce the first rapidly reversible small-molecule-based dimerization system and demonstrate a sufficiently fast switch-off to determine kinetics of lipid metabolizing enzymes in living cells. We applied this new method to induce and stop phosphatidylinositol 3-kinase (PI3K) activity, allowing us to quantitatively measure the turnover of phosphatidylinositol 3,4,5-trisphosphate (PIP3) and its downstream effectors by confocal fluorescence microscopy as well as standard biochemical methods.

Intra- and interdimeric caspase-8 self-cleavage controls strength and timing of CD95-induced apoptosis

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

Molecular cloning, overexpression and characterization of a novel water channel protein from Rhodobacter sphaeroides

Aquaporins are highly selective water channel proteins integrated into plasma membranes of single cell organisms; plant roots and stromae; eye lenses, renal and red blood cells in vertebrates. To date, only a few microbial aquaporins have been characterized and their physiological importance is not well understood. Here we report on the cloning, expression and characterization of a novel aquaporin, RsAqpZ, from a purple photosynthetic bacterium, Rhodobacter sphaeroides ATCC 17023. The protein was expressed homologously at a high yield (∼20 mg/L culture) under anaerobic photoheterotrophic growth conditions. Stopped-flow light scattering experiments demonstrated its high water permeability (0.17±0.05 cm/s) and low energy of activation for water transport (2.93±0.60 kcal/mol) in reconstituted proteoliposomes at a protein to lipid ratio (w/w) of 0.04. We developed a fluorescence correlation spectroscopy based technique and utilized a fluorescent protein fusion of RsAqpZ, to estimate the single channel water permeability of RsAqpZ as 1.24 (±0.41) x 10(-12) cm(3)/s or 4.17 (±1.38)×10(10) H2O molecules/s, which is among the highest single channel permeability reported for aquaporins. Towards application to water purification technologies, we also demonstrated functional incorporation of RsAqpZ in amphiphilic block copolymer membranes.

Evaluating strategies to normalise biological replicates of Western blot data

Western blot data are widely used in quantitative applications such as statistical testing and mathematical modelling. To ensure accurate quantitation and comparability between experiments, Western blot replicates must be normalised, but it is unclear how the available methods affect statistical properties of the data. Here we evaluate three commonly used normalisation strategies: (i) by fixed normalisation point or control; (ii) by sum of all data points in a replicate; and (iii) by optimal alignment of the replicates. We consider how these different strategies affect the coefficient of variation (CV) and the results of hypothesis testing with the normalised data. Normalisation by fixed point tends to increase the mean CV of normalised data in a manner that naturally depends on the choice of the normalisation point. Thus, in the context of hypothesis testing, normalisation by fixed point reduces false positives and increases false negatives. Analysis of published experimental data shows that choosing normalisation points with low quantified intensities results in a high normalised data CV and should thus be avoided. Normalisation by sum or by optimal alignment redistributes the raw data uncertainty in a mean-dependent manner, reducing the CV of high intensity points and increasing the CV of low intensity points. This causes the effect of normalisations by sum or optimal alignment on hypothesis testing to depend on the mean of the data tested; for high intensity points, false positives are increased and false negatives are decreased, while for low intensity points, false positives are decreased and false negatives are increased. These results will aid users of Western blotting to choose a suitable normalisation strategy and also understand the implications of this normalisation for subsequent hypothesis testing.

Lessons learned from quantitative dynamical modeling in systems biology

Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.

Depletion of ERK2 but not ERK1 abrogates oncogenic Ras-induced senescence

In response to oncogenic activation, cells initially undergo proliferation followed by an irreversible growth arrest called oncogene-induced senescence (OIS), an endogenous defense mechanism against tumorigenesis. Oncogenic activation of ERK1/2 is essential for both the initial phase of cellular proliferation as well as subsequent premature senescence, but little is known about the specific contribution of ERK1 versus 2 to OIS. Here we show that depletion of ERK2 but not ERK1 by shRNA knockdown in MEFs leads to continuous proliferation bypassing senescence even in the presence of oncogenic HRAS(V12). Upon depletion of ERK2, induction of both p19(Arf) and p16(Ink4a) was significantly compromised after oncogenic HRAS(V12) expression, attenuating activation of the key tumor suppressors p53 and pRb. Here we demonstrate that ERK2 but not ERK1 indirectly regulates p19(Arf) and p16(Ink4a) both at the transcriptional and translational level. Oncogenic Ras expression after ERK2 knockdown downregulates Fra-1 and c-Jun, components of the activator protein-1 (AP-1) heterodimer essential for transactivation of p19(Arf). Similarly we show a significant decrease in the activation of p38 MAPK and ETS family members which are involved in the induction of p16(Ink4a). The role of ERK2 in translational regulation is observed by the lack of tuberin (TSC2) and p70 ribosomal S6 kinase 1 (p70S6K1) phosphorylation, components of the mTOR pathway, which enhances p19(Arf) mRNA translation during oncogenic Ras-induced senescence. These observations suggest that ERK2 but not ERK1 contributes to upregulation of p19(Arf) and p16(Ink4a) in a transcription- and translation-dependent manner during oncogenic Ras-induced senescence. Taken together, our data indicate that ERK2 is the key ERK isoform mediating the senescence signaling pathway downstream of oncogenic Ras.

Elucidating the sources of β-catenin dynamics in human neural progenitor cells

Human neural progenitor cells (hNPCs) form a new prospect for replacement therapies in the context of neurodegenerative diseases. The Wnt/β-catenin signaling pathway is known to be involved in the differentiation process of hNPCs. RVM cells form a common cell model of hNPCs for in vitro investigation. Previous observations in RVM cells raise the question of whether observed kinetics of the Wnt/β-catenin pathway in later differentiation phases are subject to self-induced signaling. However, a concern when investigating RVM cells is that experimental results are possibly biased by the asynchrony of cells w.r.t. the cell cycle. In this paper, we present, based on experimental data, a computational modeling study on the Wnt/β-catenin signaling pathway in RVM cell populations asynchronously distributed w.r.t. to their cell cycle phases. Therefore, we derive a stochastic model of the pathway in single cells from the reference model in literature and extend it by means of cell populations and cell cycle asynchrony. Based on this, we show that the impact of the cell cycle asynchrony on wet-lab results that average over cell populations is negligible. We then further extend our model and the thus-obtained simulation results provide additional evidence that self-induced Wnt signaling occurs in RVM cells. We further report on significant stochastic effects that directly result from model parameters provided in literature and contradict experimental observations.

Targeted near-infrared imaging of the erythropoietin receptor in human lung cancer xenografts

The putative presence of the erythropoietin receptor (EpoR) on human cancer cells has given rise to controversial discussion about the use of recombinant human erythropoietin (rhuEpo) for treatment of patients with chemotherapy-induced anemia. In vivo analysis of the EpoR status in tumors could help in elucidating the role of erythropoietin in cancer. Thus, the aim of this study was to develop a targeted EpoR probe for the investigation of EpoR expression in human lung cancer xenografts by fluorescence-mediated tomography.

METHODS

Epo-Cy5.5 was generated by coupling Cy5.5 to rhuEpo. In vitro binding assays were performed using the EpoR-positive non-small cell lung cancer (NSCLC) cell lines A549 (lower EpoR expression) and H838 (higher EpoR expression), the EpoR-negative cell line H2030, and EpoR/EGFP-overexpressing HeLa cells. In vivo specificity of Epo-Cy5.5 was confirmed by competition analyses using micro-CT/fluorescence-mediated tomography fusion imaging. Biodistribution was analyzed over 50 h after injection. Binding of Epo-Cy5.5 was validated on tumor cryosections.

RESULTS

After intravenous injection, the probe was rapidly cleared from the circulation. An accumulation was observed in liver and kidneys, with a maximum at 7 h after injection followed by a decline, indicating renal excretion. Almost constant accumulation of Epo-Cy5.5 was found in bone marrow and tumors, indicating specific receptor binding. The probe allowed the discrimination between H838 with higher EpoR expression (89.54 ± 15.91 nM at 25 h) and A549 tumors with lower EpoR expression (60.45 ± 14.59 nM at 25 h, P < 0.05). Tumor accumulation of Epo-Cy5.5 could be significantly reduced by adding unlabeled rhuEpo (P < 0.05 at 4, 7, and 24 h). In vitro validation confirmed specific binding of Epo-Cy5.5 to the tumor cells, and this binding correlated with the EpoR expression level. Binding was also observed on endothelial cells. Vessel density and Epo-Cy5.5 binding on endothelial cells were comparable.

CONCLUSION

Epo-Cy5.5 allows the longitudinal analysis of EpoR expression in tumors and thereby can investigate the influence of erythropoietin on EpoR expression, tumor growth, and angiogenesis.

Dynamics and feedback loops in the transforming growth factor β signaling pathway

Transforming growth factor β (TGF-β) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-β target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.

Quantitative and kinetic profile of Wnt/β-catenin signaling components during human neural progenitor cell differentiation

ReNcell VM is an immortalized human neural progenitor cell line with the ability to differentiate in vitro into astrocytes and neurons, in which the Wnt/β-catenin pathway is known to be involved. However, little is known about kinetic changes of this pathway in human neural progenitor cell differentiation. In the present study, we provide a quantitative profile of Wnt/β-catenin pathway dynamics showing its spatio-temporal regulation during ReNcell VM cell differentiation. We show first that T-cell factor dependent transcription can be activated by stabilized β-catenin. Furthermore, endogenous Wnt ligands, pathway receptors and signaling molecules are temporally controlled, demonstrating changes related to differentiation stages. During the first three hours of differentiation the signaling molecules LRP6, Dvl2 and β-catenin are spatio-temporally regulated between distinct cellular compartments. From 24 h onward, components of the Wnt/β-catenin pathway are strongly activated and regulated as shown by mRNA up-regulation of Wnt ligands (Wnt5a and Wnt7a), receptors including Frizzled-2, -3, -6, -7, and -9, and co-receptors, and target genes including Axin2. This detailed temporal profile of the Wnt/β-catenin pathway is a first step to understand, control and to orientate, in vitro, human neural progenitor cell differentiation.

Studies on mechanisms of interferon-gamma action in pancreatic cancer using a data-driven and model-based approach

Background

Interferon-gamma (IFNγ) is a multifunctional cytokine with antifibrotic and antiproliferative efficiency. We previously found that pancreatic stellate cells (PSC), the main effector cells in cancer-associated fibrosis, are targets of IFNγ action in the pancreas. Applying a combined experimental and computational approach, we have demonstrated a pivotal role of STAT1 in IFNγ signaling in PSC. Using in vivo and in vitro models of pancreatic cancer, we have now studied IFNγ effects on the tumor cells themselves. We hypothesize that IFNγ inhibits tumor progression through two mechanisms, reduction of fibrogenesis and antiproliferative effects on the tumor cells. To elucidate the molecular action of IFNγ, we have established a mathematical model of STAT1 activation and combined experimental studies with computer simulations.

Results

In BALB/c-nu/nu mice, flank tumors composed of DSL-6A/C1 pancreatic cancer cells and PSC grew faster than pure DSL-6A/C1 cell tumors. IFNγ inhibited the growth of both types of tumors to a similar degree. Since the stroma reaction typically reduces the efficiency of therapeutic agents, these data suggested that IFNγ may retain its antitumor efficiency in PSC-containing tumors by targeting the stellate cells. Studies with cocultures of DSL-6A/C1 cells and PSC revealed a modest antiproliferative effect of IFNγ under serum-free conditions. Immunoblot analysis of STAT1 phosphorylation and confocal microscopy studies on the nuclear translocation of STAT1 in DSL-6A/C1 cells suggested that IFNγ-induced activation of the transcription factor was weaker than in PSC. The mathematical model not only reproduced the experimental data, but also underscored the conclusions drawn from the experiments by indicating that a maximum of 1/500 of total STAT1 is located as phosphorylated STAT1 in the nucleus upon IFNγ treatment of the tumor cells.

Conclusions

IFNγ is equally effective in DSL-6A/C1 tumors with and without stellate cells. While its action in the presence of PSC may be explained by inhibition of fibrogenesis, its efficiency in PSC-free tumors is unlikely to be caused by direct effects on the tumor cells alone but may involve inhibitory effects on local stroma cells as well. To gain further insights, we also plan to apply computer simulations to the analysis of tumor growth in vivo.

Small molecule GSK-3 inhibitors increase neurogenesis of human neural progenitor cells

Human neural progenitor cells provide a source for cell replacement therapy to treat neurodegenerative diseases. Therefore, there is great interest in mechanisms and tools to direct the fate of multipotent progenitor cells during their differentiation to increase the yield of a desired cell type. We tested small molecule inhibitors of glycogen synthase kinase-3 (GSK-3) for their functionality and their influence on neurogenesis using the human neural progenitor cell line ReNcell VM. Here we report the enhancement of neurogenesis of human neural progenitor cells by treatment with GSK-3 inhibitors. We tested different small molecule inhibitors of GSK-3 i.e. LiCl, sodium-valproate, kenpaullone, indirubin-3-monoxime and SB-216763 for their ability to inhibit GSK-3 in human neural progenitor cells. The highest in situ GSK-3 inhibitory effect of the drugs was found for kenpaullone and SB-216763. Accordingly, kenpaullone and SB-216763 were the only drugs tested in this study to stimulate the Wnt/β-catenin pathway that is antagonized by GSK-3. Analysis of human neural progenitor differentiation revealed an augmentation of neurogenesis by SB-216763 and kenpaullone, without changing cell cycle exit or cell survival. Small molecule inhibitors of GSK-3 enhance neurogenesis of human neural progenitor cells and may be used to direct the differentiation of neural stem and progenitor cells in therapeutic applications.

Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon α-induced early antiviral signalling

Type I interferons (IFN) are important components of the innate antiviral response. A key signalling pathway activated by IFNα is the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. Major components of the pathway have been identified. However, critical kinetic properties that facilitate accelerated initiation of intracellular antiviral signalling and thereby promote virus elimination remain to be determined. By combining mathematical modelling with experimental analysis, we show that control of dynamic behaviour is not distributed among several pathway components but can be primarily attributed to interferon regulatory factor 9 (IRF9), constituting a positive feedback loop. Model simulations revealed that increasing the initial IRF9 concentration reduced the time to peak, increased the amplitude and enhanced termination of pathway activation. These model predictions were experimentally verified by IRF9 over-expression studies. Furthermore, acceleration of signal processing was linked to more rapid and enhanced expression of IFNα target genes. Thus, the amount of cellular IRF9 is a crucial determinant for amplification of early dynamics of IFNα-mediated signal transduction.

Theoretical analysis of time-to-peak responses in biological reaction networks

Processing of information by signaling networks is characterized by properties of the induced kinetics of the activated pathway components. The maximal extent of pathway activation (maximum amplitude) and the time-to-peak-response (position) are key determinants of biological responses that have been linked to specific outcomes. We investigate how the maximum amplitude of pathway activation and its position depend on the input and wiring of a signaling network. For this purpose, we consider a simple reaction A→B that is regulated by a transient input and extended this to include back-reaction and additional partners. In particular, we show that a unique maximum of B(t) exists. Moreover, we prove that the position of the maximum is independent of the applied input but regulated by degradation reactions of B. Indeed, the time-to-peak-response decreases with increasing degradation rate, which we prove for small models and show in simulations for more complex ones. The identified dependencies provide insights into design principles that facilitate the realization dynamical characteristics like constant position of maximal pathway activation and thereby guide the characterization of unknown kinetics within larger protein networks.

Theoretical and experimental analysis links isoform-specific ERK signalling to cell fate decisions

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.

DFG Research Training Group 1387/1: dIEM oSiRiS – Integrative Development of Modelling and Simulation Methods for Regenerative Systems (DFG Graduiertenkolleg 1387/1: dIEM oSiRiS – Die integrative Entwicklung von Modellierungs- und Simulationsmethoden für regenerative Systeme)

Regenerative systems are able to overcome significant perturbations, and maintain autonomously their functionality in dynamic and uncertain environments. To analyse or develop these types of systems modelling and simulation play a crucial role. However, due to the fact of being large scale and of embracing many heterogeneously acting and interacting sub-systems, they require the development of new methodologies to support a flexible modelling at different levels of organization and abstraction and an efficient execution of experiments. These methodological developments are at the core of the DFG Research Training Group dIEM oSiRiS (The Integrative Development of Modelling and Simulation Methods for Regenerative Systems). Thereby, the analysis of characteristics and requirements of regenerative systems and the evaluation of the developed concepts are based on a concrete biological regenerative system: the exploration of signalling pathways that play a significant role in the differentiation of neural cells.

Mathematical modelling of interferon-gamma signalling in pancreatic stellate cells reflects and predicts the dynamics of STAT1 pathway activity

Signal transducer and activator of transcription (STAT) 1 is essentially involved in the mediation of antifibrotic interferon-gamma (IFN gamma) effects in pancreatic stellate cells (PSC). Here, we have further analysed the activation of the STAT1 pathway in a PSC line by combining quantitative data generation with mathematical modelling. At saturating concentrations of IFN gamma, a triphasic pattern of STAT1 activation was observed. An initial, rapid induction of phospho-STAT1 was followed by a plateau phase and another, long-lasting phase of further increase. The late increase occurred despite enhanced expression of the feedback inhibitor (SOCS1), and corresponded to increased levels of total STAT1 protein. If IFN gamma was applied at non-saturating concentrations, phospho-STAT1 and SOCS1 levels peaked and declined again over a 12 hour period, while STAT1 protein levels remained high. The mathematical model, based on a system of ordinary differential equations, describes temporal changes of the network components as a function of interactions and transport processes. The model reproduced activation profiles of all components of the STAT1 pathway that were experimentally analysed. Furthermore, it successfully predicted the dynamics of network components in additional experimental studies. Based on experimental findings and the results obtained from modelling, we suggest exhaustion of applied IFN gamma and STAT1 dephosphorylation by tyrosine phosphatases as limiting factors of STAT1 activation in PSC. In contrast, we did not obtain compelling evidence that SOCS1 acts as an efficient feedback inhibitor in our experimental system. We believe that further investigations into mathematical modelling of the STAT1 pathway will improve the understanding of the antifibrotic interferon action.

Improved semiquantitative Western blot technique with increased quantification range

With the development of new interdisciplinary fields such as systems biology, the quantitative analysis of protein expression in biological samples gains more and more importance. Although the most common method for this is ELISA, Western blot also has advantages: The separation of proteins by size allows the evaluation of only specifically bound protein. This work examines the Western blot signal chain, determines some of the parameters relevant for quantitative analysis and proposes a mathematical model of the reaction kinetics. Using this model, a semiquantitative Western blot method for simultaneous quantification of different proteins using a hyperbolic calibration curve was developed. A program was written for the purpose of hyperbolic regression that allows quick determination of the calibration curve coefficients. This program can be used also for approximation of calibration curves in other applications such as ELISA, BCA or Bradford assays.

Multistrip western blotting to increase quantitative data output

The qualitative and quantitative measurements of protein abundance and modification states are essential in understanding their functions in diverse cellular processes. Typical western blotting, though sensitive, is prone to produce substantial errors and is not readily adapted to high-throughput technologies. Multistrip western blotting is a modified immunoblotting procedure based on simultaneous electrophoretic transfer of proteins from multiple strips of polyacrylamide gels to a single membrane sheet. In comparison with the conventional technique, Multistrip western blotting increases the data output per single blotting cycle up to tenfold, allows concurrent monitoring of up to nine different proteins from the same loading of the sample, and substantially improves the data accuracy by reducing immunoblotting-derived signal errors. This approach enables statistically reliable comparison of different or repeated sets of data, and therefore is beneficial to apply in biomedical diagnostics, systems biology, and cell signaling research.

Modelling the dynamics of signalling pathways

In the present chapter we discuss methodologies for the modelling, calibration and validation of cellular signalling pathway dynamics. The discussion begins with the typical range of techniques for modelling that might be employed to go from the chemical kinetics to a mathematical model of biochemical pathways. In particular, we consider the decision-making processes involved in selecting the right mechanism and level of detail of representation of the biochemical interactions. These include the choice between (i) deterministic and stochastic chemical kinetics representations, (ii) discrete and continuous time models and (iii) representing continuous and discrete state processes. We then discuss the task of calibrating the models using information available in web-based databases. For situations in which the data are not available from existing sources we discuss model calibration based upon measured data and system identification methods. Such methods, together with mathematical modelling databases and computational tools, are often available in standard packages. We therefore make explicit mention of a range of popular and useful sites. As an example of the whole modelling and calibration process, we discuss a study of the cross-talk between the IL-1 (interleukin-1)-stimulated NF-kappaB (nuclear factor kappaB) pathway and the TGF-beta (transforming growth factor beta)-stimulated Smad2 pathway.

Quantitative protein microarrays for time-resolved measurements of protein phosphorylation

The quantitative analysis of signaling networks requires highly sensitive methods for the time-resolved determination of protein phosphorylation. For this reason, we developed a quantitative protein microarray that monitors the activation of multiple signaling pathways in parallel, and at high temporal resolution. A label-free sandwich approach was combined with near infrared detection, thus permitting the accurate quantification of low-level phosphoproteins in limited biological samples corresponding to less than 50,000 cells, and with a very low standard deviation of approximately 5%. The identification of suitable antibody pairs was facilitated by determining their accuracy and dynamic range using our customized software package Quantpro. Thus, we are providing an important tool to generate quantitative data for systems biology approaches, and to drive innovative diagnostic applications.

Putting the pieces together in diabetes research: towards a hierarchical model of whole-body glucose homeostasis

Type 2 diabetes is one of the most widespread and rapidly spreading diseases world-wide and has been subject of extensive research efforts. However, understanding the molecular basis of the disease is increasing piecemeal and a consensus regarding the overall picture of normal metabolic regulation and malfunction in diabetes has not emerged. A systems biology approach, combining mathematical modelling with simultaneous high-throughput measurements, can be of considerable help. On the whole-body level, this has been done in pharmacokinetic and pharmacodynamic models, which recently have started to mature into more physiologically realistic organ-based models. At the other end of the spectrum, detailed models for crucial cellular processes are starting to mature into complete modules that potentially can be fitted into such whole-body organ-based models. The result of such a merge is a multi-level hierarchical model, which is a model type that has been common in technical systems. In this review, we report and exemplify some of the recent progress that has been made to achieve such a hierarchical model, and we argue why it is only through such a model that a complete picture of diabetes mellitus can be obtained.

Target specificity and off-target effects as determinants of cancer drug efficacy

Targeted therapeutics are aimed to hit one or a few key cellular targets. Agents that target single signaling molecules (such as EGFR and IGF-R1) often show limited clinical activities, at least in the major groups of solid tumors. Nevertheless, some signaling inhibitors are effective in the treatment of previously difficult-to-treat diseases such as renal carcinoma. Similarly, these drugs inhibit multiple kinases and/or may display off-target activities. Inhibition of cellular targets such as the proteasome, heat-shock protein 90, and histone deacetylase induces complex cellular effects, and agents that inhibit these targets show promising clinical activities. Clinically effective targeted agents are therefore reminiscent of conventional agents such as cisplatin and doxorubicin, which are known to have several cellular targets. It is becoming increasingly clear that a comprehensive understanding of the spectrum of effects exerted by an anticancer agent is fundamental for understanding its efficacy and toxicity profile.

A systems biology approach to analyse amplification in the JAK2-STAT5 signalling pathway

Background

The amplification of signals, defined as an increase in the intensity of a signal through networks of intracellular reactions, is considered one of the essential properties in many cell signalling pathways. Despite of the apparent importance of signal amplification, there have been few attempts to formalise this concept.

Results

In this work we investigate the amplification and responsiveness of the JAK2-STAT5 pathway using a kinetic model. The recruitment of EpoR to the plasma membrane, activation by Epo, and deactivation of the EpoR/JAK2 complex are considered as well as the activation and nucleocytoplasmic shuttling of STAT5. Using qualitative biological knowledge, we first establish the structure of a general power-law model. We then generate a family of models from which we select suitable candidates. The parameter values of the model are estimated from experimental quantitative time-course data. The final model, whether it is conventional model with fixed predefined integer kinetic orders or a model with variable non-integer kinetic orders, is selected on the basis of a good agreement between simulations and the experimental data. The model is used to analyse the responsiveness and amplification properties of the pathway with sustained, transient, and oscillatory stimulation.

Conclusion

The selected kinetic model predicts that the system acts as an amplifier with maximum amplification and sensitivity for input signals whose intensity match physiological values for Epo concentration and with duration in the range of one to 100 minutes. The response of the system reaches saturation for more intense and longer stimulation with Epo. We hypothesise that these properties of the system directly relate to the saturation of Epo receptor activation, its low recruitment to the plasma membrane and intense deactivation as predicted by the model.

Dynamic pathway modeling: feasibility analysis and optimal experimental design

A major challenge in systems biology is to evaluate the feasibility of a biological research project prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. Given a null hypothesis and alternative model, as well as laboratory constraints like observable players, sample size, noise level, and stimulation options, we suggest a method to obtain a list of required experiments in order to significantly reject the null hypothesis model M0 if a specified alternative model MA is realized. For this purpose, we estimate the power to detect a violation of M0 by means of Monte Carlo simulations. Iteratively, the power is maximized over all feasible stimulations of the system using multi-experiment fitting, leading to an optimal combination of experimental settings to discriminate the null hypothesis and alternative model. We prove the importance of simultaneous modeling of combined experiments with quantitative, highly sampled in vivo measurements from the Jak/STAT5 signaling pathway in fibroblasts, stimulated with erythropoietin (Epo). Afterwards we apply the presented iterative experimental design approach to the Jak/STAT3 pathway of primary hepatocytes stimulated with IL-6. Our approach offers the possibility of deciding which scientific questions can be answered based on existing laboratory constraints. To be able to concentrate on feasible questions on account of inexpensive computational simulations yields not only enormous cost and time saving, but also helps to specify realizable, systematic research projects in advance.

A system biology approach to understand functional activity of cell communication systems

Systems Biology is the quantitative analysis of dynamic interactions among several components of a biochemical system, aiming at an understanding of the behavior of the system as a whole. From an experimental perspective, systems biology is a suitable tool to support the biologist in the process of hypotheses generation and the efficient design of experiments. In this chapter, we discuss the elements of a systems biology methodology based on the interaction between experimental biologists and theoreticians. We, furthermore, show the use of such a methodology in a case study, analyzing receptor and transcription factor modulation affecting the responsiveness of the JAK2/STAT5 pathway.

Multistrip Western blotting to increase quantitative data output

The qualitative and quantitative measurements of protein abundance and protein modification states are essential in understanding their role in diverse cellular processes. Traditional Western blotting technique, though sensitive, is prone to produce substantial errors and is not readily adapted to high-throughput technologies. We propose a modified immunoblotting procedure, which is based on simultaneous transfer of proteins from multiple gel-strips onto the same membrane, and is compatible with any conventional gel electrophoresis system. As a result, the data output per single blotting cycle can readily be increased up to ten-fold. In contrast to the traditional "one protein detection per electrophoresis cycle", this procedure allows simultaneous monitoring of up to nine different proteins. In addition to maintaining the ability to detect picogram quantities of protein, the modified system substantially improves data accuracy by reducing signal errors by two-fold. Multistrip Western blotting procedure allows making statistically reliable side-by-side comparisons of different or repeated sets of data. Compared to the traditional methods, this approach provides a more economical, reproducible, and effective procedure, facilitating the generation of large amounts of high-quality quantifiable data.

Control and regulation of gene expression: quantitative analysis of the expression of phosphoglycerate kinase in bloodstream form Trypanosoma brucei

Isoenzymes of phosphoglycerate kinase in Trypanosoma brucei are differentially expressed in its two main life stages. This study addresses how the organism manages to make sufficient amounts of the isoenzyme with the correct localization, which processes (transcription, splicing, and RNA degradation) control the levels of mRNAs, and how the organism regulates the switch in isoform expression. For this, we combined new quantitative measurements of phosphoglycerate kinase mRNA abundance, RNA precursor stability, trans splicing, and ribosome loading with published data and made a kinetic computer model. For the analysis of regulation we extended regulation analysis. Although phosphoglycerate kinase mRNAs are present at surprisingly low concentrations (e.g. 12 molecules per cell), its protein is highly abundant. Substantial control of mRNA and protein levels was exerted by both mRNA synthesis and degradation, whereas splicing and precursor degradation had little control on mRNA and protein concentrations. Yet regulation of mRNA levels does not occur by transcription, but by adjusting mRNA degradation. The contribution of splicing to regulation is negligible, as for all cases where splicing is faster than RNA precursor degradation.

An error model for protein quantification

MOTIVATION

Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting.

RESULTS

Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data. An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling. The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology.

AVAILABILITY

Matlab and R code is available from the authors on request. The data can be downloaded from our website www.fdm.uni-freiburg.de/~ckreutz/data.

Power-law models of signal transduction pathways

The mathematical modelling of signal transduction pathways has become a valuable aid to understanding the complex interactions involved in intracellular signalling mechanisms. An important aspect of the mathematical modelling process is the selection of the model type and structure. Until recently, the convention has been to use a standard kinetic model, often with the Michaelis-Menten steady state assumption. However this model form, although valuable, is only one of a number of choices, and the aim of this article is to consider the mathematical structure and essential features of an alternative model form--the power-law model. Specifically, we analyse how power-law models can be applied to increase our understanding of signal transduction pathways when there may be limited prior information. We distinguish between two kinds of power law models: a) Detailed power-law models, as a tool for investigating pathways when the structure of protein-protein interactions is completely known, and; b) Simplified power-law models, for the analysis of systems with incomplete structural information or insufficient quantitative data for generating detailed models. If sufficient data of high quality are available, the advantage of detailed power-law models is that they are more realistic representations of non-homogenous or crowded cellular environments. The advantages of the simplified power-law model formulation are illustrated using some case studies in cell signalling. In particular, the investigation on the effects of signal inhibition and feedback loops and the validation of structural hypotheses are discussed.